铁道科学与工程学报2017，Vol.14Issue(6)：1153-1160,8.
铁路路基冻胀的自适应定量预测模型
Adaptive and quantitative forecast model of railroad subgrades' frost-heaving index
摘要
Abstract
According to the quantitative forecast of the railway's frost-heaving data of cold region, the paper proposed a combined model of optimization gray and neural network. By adopting a time interval weight matrix and differential equation's background value and model's initial value, the traditional non-equal time interval GM(1,1) forecast model was optimized. The combined forecast model of optimized grey and neural network by adopting BP neural network was established to rectify the initial forecast of residual errors. The model in this paper was applied in matching and forecasting the frost-heaving data of subgrades which collected from Harbin-Dalian Passenger Railway during the time period from December 2013 to January 2014. From the results, it is found that the posterior error ratio is only 0.1086 and the average prediction error value is 1.46%, as the model accuracy value as high as 0.984. It is more accuracy than the existing GM(1,1) and BP models, which helps to realize the high-accuracy quantitative forecast of the frost-heaving.关键词
铁道工程/路基冻胀/预测/灰色优化/BP网络/组合预测Key words
railway engineering/railroad subgrades' frost-heaving index/forecast/optimal gray model/BP network/combined forecast model分类
交通工程引用本文复制引用
吴湘华,乐天晗,陈峰,吴永军..铁路路基冻胀的自适应定量预测模型[J].铁道科学与工程学报,2017,14(6):1153-1160,8.基金项目
铁道部重点资助项目(2012 G009-B) （2012 G009-B）
铁路总公司科技研究开发计划课题(2014G001-E) （2014G001-E）
